Open Source Mac Change Detection Tools
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Change Detection Tools for Mac
View 1702 business solutionsBrowse free open source Change Detection tools and projects for Mac below. Use the toggles on the left to filter open source Change Detection tools by OS, license, language, programming language, and project status.
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changed-files
Github action to retrieve all (added, copied, modified, deleted, etc.
Github action to retrieve all (added, copied, modified, deleted, renamed, type changed, unmerged, unknown) files and directories. Effortlessly track all changed files and directories relative to a target branch, preceding commit or the last remote commit returning relative paths from the project root using this GitHub action. This action solely identifies files that have undergone changes within the context of events such as pull_request, push, and more. However, it doesn't detect pending uncommitted changes created during the workflow execution. -
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Open Source Tripwire
Open Source Tripwire
Open Source Tripwire® is a security and data integrity tool for monitoring and alerting on file & directory changes. This project is based on code originally contributed by Tripwire, Inc. in 2000. A Tripwire check compares the current filesystem state against a known baseline state, and alerts on any changes it detects. The baseline and check behavior are controlled by a policy file, which specifies which files or directories to monitor, and which attributes to monitor them, such as hashes, file permissions, and ownership. When an expected change occurs, such as upgrading a package, the baseline database can be updated to the new known-good state. The policy can also be updated, for example, to reduce noise or cover a newly installed package. -
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Open-CD
A Change Detection Repo Standing on the Shoulders of Giants
Open-CD is an open source change detection toolbox based on a series of open source general vision task tools. -
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OmniPull
Just pull anything
OmniPull is a powerful, cross-platform download manager built with Python and PySide6. It provides a modern, intuitive interface for managing downloads with advanced features like multi-threading, queue management, and media extraction. -
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Paths Changes Filter
Conditionally run actions based on files modified by PR
GitHub Action that enables conditional execution of workflow steps and jobs, based on the files modified by pull request, on a feature branch, or by the recently pushed commits. Run slow tasks like integration tests or deployments only for changed components. It saves time and resources, especially in mono repo setups. GitHub workflow's built-in path filters don't allow this because they don't work on a level of individual jobs or steps. -
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on-change
Watch an object or array for changes
Watch an object or array for changes. It works recursively, so it will even detect if you modify a deep property. -
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AET
Detects visual changes on websites and performs page health checks
AET is a system that detects visual changes on websites and performs basic page health checks (like w3c compliance, accessibility, HTTP status codes, JS Error checks and others). AET is designed as a flexible system that can be adapted and tailored to the regression requirements of a given project. The tool has been developed to aid front-end client-side layout regression testing of websites or portfolios, in essence assessing the impact or change of a website from one snapshot to the next. -
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ChangeFormer
A Transformer-Based Siamese Network for Change Detection
Here, we provide the PyTorch implementation of the paper: A Transformer-Based Siamese Network for Change Detection. -
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Changelog CI
Changelog CI is a GitHub Action that enables a project
Changelog CI is a GitHub Action that enables a project to automatically generate changelogs. Changelog CI can be triggered on pull_request, workflow_dispatch, and any other events that can provide the required inputs. Changelog CI uses python and GitHub API to generate a changelog for a repository. First, it tries to get the latest release from the repository (If available). Then, it checks all the pull requests/commits merged after the last release using the GitHub API. After that, it parses the data and generates the changelog. It is able to use Markdown or reStructuredText to generate a Changelog. Finally, It writes the generated changelog at the beginning of the CHANGELOG.md/CHANGELOG.rst (or user-provided filename) file. In addition to that, if a user provides a configuration file (JSON/YAML), Changelog CI parses the user-provided configuration file and renders the changelog according to user's configuration. -
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DDPM-CD
Remote sensing change detection using denoising diffusion models
This is the Pytorch implementation of Remote Sensing Change Detection using Denoising Diffusion Probabilistic Models. The generated images contain objects that we commonly see in real remote sensing images, such as buildings, trees, roads, vegetation, water surfaces, etc., demonstrating the powerful ability of the diffusion models to extract key semantics that can be further used in remote sensing change detection. We fine-tune a light-weight change detection head which takes multi-level feature representations from the pre-trained diffusion model as inputs and outputs change prediction map. -
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Frouros
Frouros is an open-source Python library for drift detection
Frouros is a Python library for drift detection in machine learning systems that provides a combination of classical and more recent algorithms for both concept and data drift detection. -
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Immutable.js
Immutable collections for JavaScript
Immutable.js offers a collection of Persistent Immutable data structures for JavaScript. Immutable data is unchangeable once created, which makes application development so much simpler. There’s no defensive copying, and you get advanced memoization and change detection techniques with simple logic. Persistent data gives you a mutative API, one that doesn’t update data in-place but always produces new and updated data. The data structures that Immutable.js provides include List, Stack, Map, OrderedMap, Set and more. These structures are highly efficient on modern JavaScript VMs, and minimize the need to cache or copy data.
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